7 papers
On the Depth Scalability of Logic Gate Networks
Taegun An, Dohun kim, Haebeom Lee +1
Logic Gate Networks (LGNs) compute through compositions of Boolean operations, yet existing LGNs do not reliably benefit from increased depth. We identify two causes: optimization…
SCOPE: Entanglement Frontier Escape for Source-Free Class Unlearning
Junhao Cai, Dohun Kim, Sung Il Choi +4
Source-free class unlearning erases whole classes using only the forget data, judged at the representation level, where features can leak a class the head no longer predicts. Exist…
Retain-Neutral Surrogates for Min-Max Unlearning
Junhao Cai, Dohun Kim, Dowon Kim +4
Machine unlearning seeks to remove the influence of designated training data while preserving performance on the remaining data. Approximate unlearning can be viewed as a local edi…
HCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compression
Junhao Cai, Taegun An, Chengjun Jin +3
Distributed multi-stage image compression -- where visual content traverses multiple processing nodes under varying quality requirements -- poses challenges. Progressive methods en…
Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning
Minkwon Lee, Hyoil Kim, Changhee Joo
Federated learning (FL) is a decentralized AI mechanism suitable for a large number of devices like in smart IoT. A major challenge of FL is the non-IID dataset problem, originatin…
Toward Scalable and Efficient Visual Data Transmission in 6G Networks
Junhao Cai, Taegun An, Changhee Joo
6G network technology will emerge in a landscape where visual data transmissions dominate global mobile traffic and are expected to grow continuously, driven by the increasing dema…
CycleGANAS: Differentiable Neural Architecture Search for CycleGAN
Taegun An, Changhee Joo
We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adv…